Overview

Dataset statistics

Number of variables12
Number of observations15
Missing cells32
Missing cells (%)17.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory159.5 B

Variable types

NUM10
CAT2

Warnings

Stormwater Retrofits: Pounds of Nitrogen Removed is highly correlated with Certified Cover Crops Acres Planted in Chesapeake Bay Watershed (Annually) and 6 other fieldsHigh correlation
Certified Cover Crops Acres Planted in Chesapeake Bay Watershed (Annually) is highly correlated with Stormwater Retrofits: Pounds of Nitrogen Removed and 2 other fieldsHigh correlation
Stormwater Retrofits: Existing Pounds of Nitrogen Removed is highly correlated with Certified Cover Crops Acres Planted in Chesapeake Bay Watershed (Annually) and 6 other fieldsHigh correlation
Wastewater Treatment Plants Retrofitted for Enhanced Nutrient Removal: Total Retrofits is highly correlated with Certified Cover Crops Acres Planted in Chesapeake Bay Watershed (Annually) and 6 other fieldsHigh correlation
Wastewater Treatment Plants Retrofitted for Enhanced Nutrient Removal: Existing Retrofits is highly correlated with Stormwater Retrofits: Pounds of Nitrogen Removed and 5 other fieldsHigh correlation
Natural Filters on Private Lands: Total Acres is highly correlated with Stormwater Retrofits: Pounds of Nitrogen Removed and 4 other fieldsHigh correlation
Natural Filters on Private Lands: Existing Acres is highly correlated with Stormwater Retrofits: Pounds of Nitrogen Removed and 4 other fieldsHigh correlation
Agricultural Stream Protection (Acres) is highly correlated with Stormwater Retrofits: Pounds of Nitrogen Removed and 3 other fieldsHigh correlation
Stormwater Retrofits: Pounds of Nitrogen Removed has 3 (20.0%) missing values Missing
Stormwater Retrofits: New Pounds of Nitrogen Removed has 3 (20.0%) missing values Missing
Stormwater Retrofits: Existing Pounds of Nitrogen Removed has 3 (20.0%) missing values Missing
Wastewater Treatment Plants Retrofitted for Enhanced Nutrient Removal: Total Retrofits has 7 (46.7%) missing values Missing
Wastewater Treatment Plants Retrofitted for Enhanced Nutrient Removal: New Retrofits has 7 (46.7%) missing values Missing
Wastewater Treatment Plants Retrofitted for Enhanced Nutrient Removal: Existing Retrofits has 7 (46.7%) missing values Missing
Agricultural Stream Protection (Acres) has 2 (13.3%) missing values Missing
Fiscal Year has unique values Unique
Natural Filters on Private Lands: Total Acres has unique values Unique
Natural Filters on Private Lands: New Acres has unique values Unique
Natural Filters on Private Lands: Existing Acres has unique values Unique
Stormwater Retrofits: Existing Pounds of Nitrogen Removed has 1 (6.7%) zeros Zeros
Wastewater Treatment Plants Retrofitted for Enhanced Nutrient Removal: Existing Retrofits has 1 (6.7%) zeros Zeros
Natural Filters on Private Lands: Existing Acres has 1 (6.7%) zeros Zeros

Reproduction

Analysis started2020-12-13 00:00:59.998843
Analysis finished2020-12-13 00:01:07.831583
Duration7.83 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Fiscal Year
Categorical

UNIQUE

Distinct15
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size248.0 B
FY2012
 
1
FY2014
 
1
FY2002
 
1
FY2003
 
1
FY2001
 
1
Other values (10)
10 
ValueCountFrequency (%) 
FY201216.7%
 
FY201416.7%
 
FY200216.7%
 
FY200316.7%
 
FY200116.7%
 
FY200816.7%
 
FY200716.7%
 
FY200016.7%
 
FY201316.7%
 
FY200616.7%
 
FY200916.7%
 
FY201016.7%
 
FY201116.7%
 
FY200516.7%
 
FY200416.7%
 
2020-12-12T19:01:07.898141image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique15 ?
Unique (%)100.0%
2020-12-12T19:01:07.969202image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length6
Min length6

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
02730.0%
 
21718.9%
 
F1516.7%
 
Y1516.7%
 
177.8%
 
322.2%
 
422.2%
 
511.1%
 
611.1%
 
711.1%
 
811.1%
 
911.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number6066.7%
 
Uppercase Letter3033.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
F1550.0%
 
Y1550.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
02745.0%
 
21728.3%
 
1711.7%
 
323.3%
 
423.3%
 
511.7%
 
611.7%
 
711.7%
 
811.7%
 
911.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Common6066.7%
 
Latin3033.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
F1550.0%
 
Y1550.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
02745.0%
 
21728.3%
 
1711.7%
 
323.3%
 
423.3%
 
511.7%
 
611.7%
 
711.7%
 
811.7%
 
911.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII90100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
02730.0%
 
21718.9%
 
F1516.7%
 
Y1516.7%
 
177.8%
 
322.2%
 
422.2%
 
511.1%
 
611.1%
 
711.1%
 
811.1%
 
911.1%
 
Distinct14
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203601.7333
Minimum31335
Maximum410676
Zeros0
Zeros (%)0.0%
Memory size248.0 B
2020-12-12T19:01:08.031255image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum31335
5-th percentile46011.9
Q1105691.5
median179366
Q3308889.5
95-th percentile408481.5
Maximum410676
Range379341
Interquartile range (IQR)203198

Descriptive statistics

Standard deviation135522.9681
Coefficient of variation (CV)0.6656277718
Kurtosis-1.09442662
Mean203601.7333
Median Absolute Deviation (MAD)81611
Skewness0.5948284154
Sum3054026
Variance1.836647488e+10
MonotocityNot monotonic
2020-12-12T19:01:08.096812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%) 
179366213.3%
 
15977316.7%
 
11362816.7%
 
9775516.7%
 
38479416.7%
 
23298516.7%
 
40754116.7%
 
41067616.7%
 
5230216.7%
 
7092016.7%
 
20471316.7%
 
40407216.7%
 
3133516.7%
 
12480016.7%
 
ValueCountFrequency (%) 
3133516.7%
 
5230216.7%
 
7092016.7%
 
9775516.7%
 
11362816.7%
 
12480016.7%
 
15977316.7%
 
179366213.3%
 
20471316.7%
 
23298516.7%
 
ValueCountFrequency (%) 
41067616.7%
 
40754116.7%
 
40407216.7%
 
38479416.7%
 
23298516.7%
 
20471316.7%
 
179366213.3%
 
15977316.7%
 
12480016.7%
 
11362816.7%
 

Stormwater Retrofits: Pounds of Nitrogen Removed
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct12
Distinct (%)100.0%
Missing3
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean131340.3333
Minimum26101
Maximum239583
Zeros0
Zeros (%)0.0%
Memory size248.0 B
2020-12-12T19:01:08.167372image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum26101
5-th percentile35060.5
Q170616.25
median119607
Q3194852.25
95-th percentile230233.55
Maximum239583
Range213482
Interquartile range (IQR)124236

Descriptive statistics

Standard deviation74921.76912
Coefficient of variation (CV)0.5704399191
Kurtosis-1.620786347
Mean131340.3333
Median Absolute Deviation (MAD)70970
Skewness0.0689731195
Sum1576084
Variance5613271488
MonotocityNot monotonic
2020-12-12T19:01:08.227424image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
4239116.7%
 
22258416.7%
 
23958316.7%
 
2610116.7%
 
13245316.7%
 
7470616.7%
 
20131516.7%
 
18845616.7%
 
9068916.7%
 
10676116.7%
 
5834716.7%
 
19269816.7%
 
(Missing)320.0%
 
ValueCountFrequency (%) 
2610116.7%
 
4239116.7%
 
5834716.7%
 
7470616.7%
 
9068916.7%
 
10676116.7%
 
13245316.7%
 
18845616.7%
 
19269816.7%
 
20131516.7%
 
ValueCountFrequency (%) 
23958316.7%
 
22258416.7%
 
20131516.7%
 
19269816.7%
 
18845616.7%
 
13245316.7%
 
10676116.7%
 
9068916.7%
 
7470616.7%
 
5834716.7%
 
Distinct12
Distinct (%)100.0%
Missing3
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean19965.25
Minimum4242
Maximum56003
Zeros0
Zeros (%)0.0%
Memory size248.0 B
2020-12-12T19:01:08.286975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum4242
5-th percentile6648.25
Q115976.25
median16324.5
Q322374.75
95-th percentile39556.9
Maximum56003
Range51761
Interquartile range (IQR)6398.5

Descriptive statistics

Standard deviation12895.61796
Coefficient of variation (CV)0.6459031547
Kurtosis6.040917175
Mean19965.25
Median Absolute Deviation (MAD)2809.5
Skewness2.114470701
Sum239583
Variance166296962.6
MonotocityNot monotonic
2020-12-12T19:01:08.348528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
1699916.7%
 
1635916.7%
 
2610116.7%
 
2569216.7%
 
1598316.7%
 
1629016.7%
 
5600316.7%
 
2126916.7%
 
861716.7%
 
424216.7%
 
1607216.7%
 
1595616.7%
 
(Missing)320.0%
 
ValueCountFrequency (%) 
424216.7%
 
861716.7%
 
1595616.7%
 
1598316.7%
 
1607216.7%
 
1629016.7%
 
1635916.7%
 
1699916.7%
 
2126916.7%
 
2569216.7%
 
ValueCountFrequency (%) 
5600316.7%
 
2610116.7%
 
2569216.7%
 
2126916.7%
 
1699916.7%
 
1635916.7%
 
1629016.7%
 
1607216.7%
 
1598316.7%
 
1595616.7%
 

Stormwater Retrofits: Existing Pounds of Nitrogen Removed
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct12
Distinct (%)100.0%
Missing3
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean111375.0833
Minimum0
Maximum222584
Zeros1
Zeros (%)6.7%
Memory size248.0 B
2020-12-12T19:01:08.411082image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14355.55
Q154358
median98725
Q3189516.5
95-th percentile210886.05
Maximum222584
Range222584
Interquartile range (IQR)135158.5

Descriptive statistics

Standard deviation75375.7015
Coefficient of variation (CV)0.676773469
Kurtosis-1.412316705
Mean111375.0833
Median Absolute Deviation (MAD)64479
Skewness0.1454022947
Sum1336501
Variance5681496377
MonotocityNot monotonic
2020-12-12T19:01:08.471634image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
4239116.7%
 
22258416.7%
 
18845616.7%
 
13245316.7%
 
10676116.7%
 
7470616.7%
 
20131516.7%
 
2610116.7%
 
9068916.7%
 
19269816.7%
 
5834716.7%
 
016.7%
 
(Missing)320.0%
 
ValueCountFrequency (%) 
016.7%
 
2610116.7%
 
4239116.7%
 
5834716.7%
 
7470616.7%
 
9068916.7%
 
10676116.7%
 
13245316.7%
 
18845616.7%
 
19269816.7%
 
ValueCountFrequency (%) 
22258416.7%
 
20131516.7%
 
19269816.7%
 
18845616.7%
 
13245316.7%
 
10676116.7%
 
9068916.7%
 
7470616.7%
 
5834716.7%
 
4239116.7%
 
Distinct8
Distinct (%)100.0%
Missing7
Missing (%)46.7%
Infinite0
Infinite (%)0.0%
Mean14.75
Minimum2
Maximum30
Zeros0
Zeros (%)0.0%
Memory size248.0 B
2020-12-12T19:01:08.531185image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.05
Q17.25
median12
Q323.75
95-th percentile28.6
Maximum30
Range28
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation10.33371735
Coefficient of variation (CV)0.7005910068
Kurtosis-1.518016488
Mean14.75
Median Absolute Deviation (MAD)8.5
Skewness0.3740070308
Sum118
Variance106.7857143
MonotocityNot monotonic
2020-12-12T19:01:08.588234image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
2316.7%
 
2616.7%
 
3016.7%
 
1316.7%
 
1116.7%
 
816.7%
 
516.7%
 
216.7%
 
(Missing)746.7%
 
ValueCountFrequency (%) 
216.7%
 
516.7%
 
816.7%
 
1116.7%
 
1316.7%
 
2316.7%
 
2616.7%
 
3016.7%
 
ValueCountFrequency (%) 
3016.7%
 
2616.7%
 
2316.7%
 
1316.7%
 
1116.7%
 
816.7%
 
516.7%
 
216.7%
 
Distinct4
Distinct (%)50.0%
Missing7
Missing (%)46.7%
Memory size248.0 B
3
2
10
4
ValueCountFrequency (%) 
3426.7%
 
2213.3%
 
1016.7%
 
416.7%
 
(Missing)746.7%
 
2020-12-12T19:01:08.663299image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)25.0%
2020-12-12T19:01:08.712341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:08.766888image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length3.066666667
Min length3

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n1430.4%
 
0919.6%
 
.817.4%
 
a715.2%
 
348.7%
 
224.3%
 
412.2%
 
112.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter2145.7%
 
Decimal Number1737.0%
 
Other Punctuation817.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n1466.7%
 
a733.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0952.9%
 
3423.5%
 
2211.8%
 
415.9%
 
115.9%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.8100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common2554.3%
 
Latin2145.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n1466.7%
 
a733.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
0936.0%
 
.832.0%
 
3416.0%
 
228.0%
 
414.0%
 
114.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII46100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n1430.4%
 
0919.6%
 
.817.4%
 
a715.2%
 
348.7%
 
224.3%
 
412.2%
 
112.2%
 
Distinct8
Distinct (%)100.0%
Missing7
Missing (%)46.7%
Infinite0
Infinite (%)0.0%
Mean11
Minimum0
Maximum26
Zeros1
Zeros (%)6.7%
Memory size248.0 B
2020-12-12T19:01:08.825439image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.7
Q14.25
median9.5
Q315.5
95-th percentile24.95
Maximum26
Range26
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation9.411239481
Coefficient of variation (CV)0.8555672256
Kurtosis-0.7937023933
Mean11
Median Absolute Deviation (MAD)6
Skewness0.6416487105
Sum88
Variance88.57142857
MonotocityNot monotonic
2020-12-12T19:01:08.888493image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
1316.7%
 
2316.7%
 
2616.7%
 
1116.7%
 
816.7%
 
516.7%
 
216.7%
 
016.7%
 
(Missing)746.7%
 
ValueCountFrequency (%) 
016.7%
 
216.7%
 
516.7%
 
816.7%
 
1116.7%
 
1316.7%
 
2316.7%
 
2616.7%
 
ValueCountFrequency (%) 
2616.7%
 
2316.7%
 
1316.7%
 
1116.7%
 
816.7%
 
516.7%
 
216.7%
 
016.7%
 

Natural Filters on Private Lands: Total Acres
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct15
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78039.26667
Minimum13618
Maximum114356
Zeros0
Zeros (%)0.0%
Memory size248.0 B
2020-12-12T19:01:08.953051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum13618
5-th percentile23688.9
Q165548
median81512
Q3104335
95-th percentile112692.8
Maximum114356
Range100738
Interquartile range (IQR)38787

Descriptive statistics

Standard deviation30522.15344
Coefficient of variation (CV)0.391112766
Kurtosis-0.001467947841
Mean78039.26667
Median Absolute Deviation (MAD)20999
Skewness-0.8003695748
Sum1170589
Variance931601850.8
MonotocityNot monotonic
2020-12-12T19:01:09.016603image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%) 
8655916.7%
 
4548716.7%
 
6997316.7%
 
7672516.7%
 
11435616.7%
 
1361816.7%
 
10251116.7%
 
10966716.7%
 
11198016.7%
 
7951916.7%
 
10615916.7%
 
8151216.7%
 
2800516.7%
 
8339516.7%
 
6112316.7%
 
ValueCountFrequency (%) 
1361816.7%
 
2800516.7%
 
4548716.7%
 
6112316.7%
 
6997316.7%
 
7672516.7%
 
7951916.7%
 
8151216.7%
 
8339516.7%
 
8655916.7%
 
ValueCountFrequency (%) 
11435616.7%
 
11198016.7%
 
10966716.7%
 
10615916.7%
 
10251116.7%
 
8655916.7%
 
8339516.7%
 
8151216.7%
 
7951916.7%
 
7672516.7%
 
Distinct15
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7623.733333
Minimum1883
Maximum17482
Zeros0
Zeros (%)0.0%
Memory size248.0 B
2020-12-12T19:01:09.079657image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1883
5-th percentile1960
Q12585
median3648
Q314002.5
95-th percentile16411
Maximum17482
Range15599
Interquartile range (IQR)11417.5

Descriptive statistics

Standard deviation6041.780455
Coefficient of variation (CV)0.7924962995
Kurtosis-1.552023568
Mean7623.733333
Median Absolute Deviation (MAD)1765
Skewness0.5904784597
Sum114356
Variance36503111.07
MonotocityNot monotonic
2020-12-12T19:01:09.142211image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%) 
316416.7%
 
188316.7%
 
885016.7%
 
1563616.7%
 
1438716.7%
 
1361816.7%
 
1595216.7%
 
364816.7%
 
350816.7%
 
279416.7%
 
199316.7%
 
237616.7%
 
231316.7%
 
1748216.7%
 
675216.7%
 
ValueCountFrequency (%) 
188316.7%
 
199316.7%
 
231316.7%
 
237616.7%
 
279416.7%
 
316416.7%
 
350816.7%
 
364816.7%
 
675216.7%
 
885016.7%
 
ValueCountFrequency (%) 
1748216.7%
 
1595216.7%
 
1563616.7%
 
1438716.7%
 
1361816.7%
 
885016.7%
 
675216.7%
 
364816.7%
 
350816.7%
 
316416.7%
 

Natural Filters on Private Lands: Existing Acres
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE
ZEROS

Distinct15
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70415.53333
Minimum0
Maximum111980
Zeros1
Zeros (%)6.7%
Memory size248.0 B
2020-12-12T19:01:09.212772image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9532.6
Q153305
median79519
Q394535
95-th percentile110360.9
Maximum111980
Range111980
Interquartile range (IQR)41230

Descriptive statistics

Standard deviation34786.93133
Coefficient of variation (CV)0.4940235441
Kurtosis-0.2579164745
Mean70415.53333
Median Absolute Deviation (MAD)22992
Skewness-0.7932527073
Sum1056233
Variance1210130592
MonotocityNot monotonic
2020-12-12T19:01:09.276327image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%) 
7951916.7%
 
10615916.7%
 
8655916.7%
 
10251116.7%
 
6997316.7%
 
7672516.7%
 
1361816.7%
 
4548716.7%
 
10966716.7%
 
11198016.7%
 
8151216.7%
 
2800516.7%
 
8339516.7%
 
6112316.7%
 
016.7%
 
ValueCountFrequency (%) 
016.7%
 
1361816.7%
 
2800516.7%
 
4548716.7%
 
6112316.7%
 
6997316.7%
 
7672516.7%
 
7951916.7%
 
8151216.7%
 
8339516.7%
 
ValueCountFrequency (%) 
11198016.7%
 
10966716.7%
 
10615916.7%
 
10251116.7%
 
8655916.7%
 
8339516.7%
 
8151216.7%
 
7951916.7%
 
7672516.7%
 
6997316.7%
 

Agricultural Stream Protection (Acres)
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct13
Distinct (%)100.0%
Missing2
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean34101.53846
Minimum26941
Maximum49448
Zeros0
Zeros (%)0.0%
Memory size248.0 B
2020-12-12T19:01:09.343384image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum26941
5-th percentile27232
Q127899
median31266
Q336792
95-th percentile47177
Maximum49448
Range22507
Interquartile range (IQR)8893

Descriptive statistics

Standard deviation7456.936409
Coefficient of variation (CV)0.2186686216
Kurtosis-0.1053585388
Mean34101.53846
Median Absolute Deviation (MAD)3840
Skewness0.998302565
Sum443320
Variance55605900.6
MonotocityNot monotonic
2020-12-12T19:01:09.405438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
2789916.7%
 
2766216.7%
 
4944816.7%
 
3126616.7%
 
3679216.7%
 
3376716.7%
 
4566316.7%
 
3603816.7%
 
3005716.7%
 
2742616.7%
 
2868116.7%
 
4168016.7%
 
2694116.7%
 
(Missing)213.3%
 
ValueCountFrequency (%) 
2694116.7%
 
2742616.7%
 
2766216.7%
 
2789916.7%
 
2868116.7%
 
3005716.7%
 
3126616.7%
 
3376716.7%
 
3603816.7%
 
3679216.7%
 
ValueCountFrequency (%) 
4944816.7%
 
4566316.7%
 
4168016.7%
 
3679216.7%
 
3603816.7%
 
3376716.7%
 
3126616.7%
 
3005716.7%
 
2868116.7%
 
2789916.7%
 

Interactions

2020-12-12T19:01:00.349144image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:00.419705image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:00.490266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:00.555822image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:00.625382image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:00.690939image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:00.757496image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:00.825554image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:00.895615image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:00.962672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:01.031732image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:01.100791image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:01.170851image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:01.236408image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:01.305968image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:01.374027image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:01.444587image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:01.514647image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:01.585709image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:01.652266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:01.721325image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:01.784880image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:01.848935image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:01.909988image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:01.975044image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:02.037597image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:02.100151image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:02.164206image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:02.228762image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:02.290315image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:02.358373image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:02.428934image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:02.500996image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:02.567553image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:02.637614image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:02.704671image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:02.772230image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:02.841289image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:02.912850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:02.981910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:03.052471image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:03.118027image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:03.185085image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:03.247138image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:03.314196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:03.376750image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:03.440805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:03.506862image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:03.573419image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:03.636974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:03.703531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:03.769088image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:03.834644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:03.897698image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:03.964255image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:04.027810image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:04.091365image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:04.160925image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:04.228483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:04.294539image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:04.361097image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:04.430156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:04.500717image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:04.565773image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:04.634832image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:04.701390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:04.767447image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:04.835005image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:04.904564image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:04.971622image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:05.041182image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:05.111743image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:05.182303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:05.249862image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:05.320923image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:05.388481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:05.458041image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:05.530103image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:05.603166image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:05.671224image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:05.741785image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:05.808342image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:05.874900image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:05.938454image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:06.006012image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:06.069567image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:06.134123image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:06.201180image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:06.268738image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:06.334295image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:06.402854image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:06.473915image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:06.544976image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:06.612034image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:06.684596image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:06.752655image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:06.820714image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:06.890774image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:06.962836image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:07.033396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-12T19:01:09.476499image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T19:01:09.677171image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T19:01:09.876343image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T19:01:10.080519image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-12T19:01:10.270682image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-12T19:01:07.181524image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:07.367184image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:07.538831image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:01:07.674448image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

Fiscal YearCertified Cover Crops Acres Planted in Chesapeake Bay Watershed (Annually)Stormwater Retrofits: Pounds of Nitrogen RemovedStormwater Retrofits: New Pounds of Nitrogen RemovedStormwater Retrofits: Existing Pounds of Nitrogen RemovedWastewater Treatment Plants Retrofitted for Enhanced Nutrient Removal: Total RetrofitsWastewater Treatment Plants Retrofitted for Enhanced Nutrient Removal: New RetrofitsWastewater Treatment Plants Retrofitted for Enhanced Nutrient Removal: Existing RetrofitsNatural Filters on Private Lands: Total AcresNatural Filters on Private Lands: New AcresNatural Filters on Private Lands: Existing AcresAgricultural Stream Protection (Acres)
0FY2000159773NaNNaNNaNNaNNaNNaN1361813618026941.0
1FY200170920NaNNaNNaNNaNNaNNaN28005143871361827426.0
2FY200297755NaNNaNNaNNaNNaNNaN45487174822800527662.0
3FY200311362826101.026101.00.0NaNNaNNaN61123156364548727899.0
4FY20043133542391.016290.026101.0NaNNaNNaN6997388506112328681.0
5FY20055230258347.015956.042391.0NaNNaNNaN7672567526997330057.0
6FY200612480074706.016359.058347.02.02.00.07951927947672531266.0
7FY200723298590689.015983.074706.05.03.02.08151219937951933767.0
8FY2008179366106761.016072.090689.08.03.05.08339518838151236038.0
9FY2009179366132453.025692.0106761.011.03.08.08655931648339536792.0

Last rows

Fiscal YearCertified Cover Crops Acres Planted in Chesapeake Bay Watershed (Annually)Stormwater Retrofits: Pounds of Nitrogen RemovedStormwater Retrofits: New Pounds of Nitrogen RemovedStormwater Retrofits: Existing Pounds of Nitrogen RemovedWastewater Treatment Plants Retrofitted for Enhanced Nutrient Removal: Total RetrofitsWastewater Treatment Plants Retrofitted for Enhanced Nutrient Removal: New RetrofitsWastewater Treatment Plants Retrofitted for Enhanced Nutrient Removal: Existing RetrofitsNatural Filters on Private Lands: Total AcresNatural Filters on Private Lands: New AcresNatural Filters on Private Lands: Existing AcresAgricultural Stream Protection (Acres)
5FY20055230258347.015956.042391.0NaNNaNNaN7672567526997330057.0
6FY200612480074706.016359.058347.02.02.00.07951927947672531266.0
7FY200723298590689.015983.074706.05.03.02.08151219937951933767.0
8FY2008179366106761.016072.090689.08.03.05.08339518838151236038.0
9FY2009179366132453.025692.0106761.011.03.08.08655931648339536792.0
10FY2010204713188456.056003.0132453.013.02.011.0102511159528655941680.0
11FY2013404072222584.021269.0201315.030.04.026.01119802313109667NaN
12FY2012410676201315.08617.0192698.026.03.023.0109667350810615949448.0
13FY2011384794192698.04242.0188456.023.010.013.0106159364810251145663.0
14FY2014407541239583.016999.0222584.0NaNNaNNaN1143562376111980NaN